normal

Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range
of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic
Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic
volatility models for univariate time series. Prior specifications are flexible and explicitly encourage
users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed
and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes
factor and leave-one-out cross-validation methods. References: Hyndman (2017)
<doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Asael Alonzo Matamoros

bayesforecast

Bayesian Time Series Modeling with Stan

Cristian Cruz Torres

Andres Dala

Rob Hyndman

Mitchell O'Hara-Wild

normal function

<dl><dt>mu</dt>
<dd>the location parameter mu</dd>
<dt>sd</dt>
<dd>the standard deviation parameter sigma</dd></dl>

Arguments

Define a normal prior distribution — normal

<dl>

<dt>mu</dt>
<dd>the location parameter mu</dd>


<dt>sd</dt>
<dd>the standard deviation parameter sigma</dd>

</dl>

Define a normal prior distribution

normal: Define a normal prior distribution

Description

Usage

Value

Arguments

Details